Replication of below article’s Data and Visualizations
“Has International Travel
to the U.S. Really Collapsed?”
By Josh Holder, Niraj Chokshi and Samuel Granados
Karim K. Kardous

International Travel Into the US Prelude

About the data/article in a nutshell: This New York Times article, backed by numbers from government agencies for the US & Canada, attempts to show whether International Travel into the US has dipped as a result of President Trump’s administration and policies relating to broad tariffs, tight border control, etc.
The main takeaway is that while travel originating from Asia into the United States has timidly increased, that from Europe has stalled, meanwhile that of Canada has sharply decreased, especially when it comes to car crossings into the US; relative to air travel.
Despite a drastic drop for US bound travel from Canada, overall, travel into the United States has remained fairly undisturbed.

If you like to give the original article a read, you can find it here.

Overall Strategy for building first plot: One way to recreate the first visual; which shows in a report-card style the % change in flight bookings into the US comparing (Jan 1st through April 26, 2024) to (same period this year), and looking at 1) overall (International), 2) European, 3) Asian, and 4) Canadian inbound travel; is to generate 4 tiles with a subtle vertical tick/separator between each of those said 4 percentages. The time frame of visits for both years covers Summer; which allows for ‘apples to apples’ comparisons but also focuses on an upcoming period of the year - very near future, meaning that said bookings are more likely than not to be definitive for a vast majority of them.

#|echo: false
#|message: false
#|warning: false
#|include: false
# check if the required package 'emo' is installed;
# if not, it might mean your renv environment is not fully restored.
# running `renv::restore()` will install all necessary packages
# to ensure consistent package versions for building this quarto document,
# effectively 'containerizing' your project and protecting it from future package changes.
if (!requireNamespace("emo", quietly = TRUE)) {
  message("\nIt looks like your environment might not be restored.\nRun `renv::restore()` to install required packages.\n")
}

# load packages
library(xml2)
library(downlit)
library(gdtools)
library(tidyverse)
library(quarto)
library(chromote)
library(here)
library(tidycensus)
library(janitor)
library(purrr)
library(ggtext)
library(ggshadow)
library(ggiraph)
library(gfonts)
library(showtext)
library(ggborderline)
library(grid)
library(patchwork)
library(shiny)
library(rvest)
library(htmltools)
library(gt)
library(rsvg)
library(magick)
library(stringr)
library(ggimage)
library(emo)


font_add(family = "franklin-medium", regular = "renv/library/macos/R-4.5/aarch64-apple-darwin20/sysfonts/fonts/Libre_Franklin/static/LibreFranklin-Medium.ttf") 

theme_set_custom <- function() {
  
  # loading google Fonts
  sysfonts::font_add_google("Libre Franklin", "franklin")
  sysfonts::font_add(
    family = "franklin-medium", 
    regular = "renv/library/macos/R-4.5/aarch64-apple-darwin20/sysfonts/fonts/Libre_Franklin/static/LibreFranklin-Medium.ttf"
  )
  showtext::showtext_auto()

  # applying ggplot2 theme
  ggplot2::theme_set(
    ggplot2::theme_minimal(base_family = "franklin") +
      ggplot2::theme(
        panel.background = ggplot2::element_rect(fill = "#F9F9F9", color = NA),
        plot.background = ggplot2::element_rect(fill = "#F9F9F9", color = NA)
      )
  )
}

theme_set_custom()

Travel Score Cards

#|echo: false
#|message: false
#|warning: false
#|include: true

theme_set_custom()

p1_tribble <- tribble(
  ~perc_change, ~label, ~region, ~fill, ~width_cm, ~ length_cm, 
  '-1.5%', 'International arrivals\n at U.S. airports', 'International','#969696', 170 / 300 * 2.54, 80 / 300 * 2.54, # converting into actual cm, controlling for resolution set (300 or print quality)
  '-2%', 'Summer flight\n bookings from Europe', 'Europe', '#969696', 130 / 300 * 2.54, 77 / 300 * 2.54,
  '+4%', 'Summer flight\n bookings from Asia', 'Asia', '#2b9d6c', 130 / 300 * 2.54, 77/ 300 * 2.54,
  '-21%', 'Summer flight\n bookings from Canada', 'Canada', '#d65f00', 164 / 300 * 2.54, 77 / 300 * 2.54
)

# here i go for an interactive process whereby each plot is created separately in a list of plots; one main reason is that the boxes/rectangles are of different sizes. 
tile_plot_rounded <- function(perc_change, label_text, fill, width_cm, length_cm, scaler = 3) { # 3 was best here
  library(grid) # for some reason, roundrectGrob() wasn't running without first loading grid here too (even if called earlier when loading all packages)
  ggplot() +
    ggtitle(label_text) + # set the label as thes plot sub-title
    annotation_custom(
      grob = roundrectGrob(
        width = unit(width_cm * scaler, "cm"),    
        height = unit(length_cm * scaler, "cm"), 
        r = unit(0.1, "npc"),  # corner radius, the higher the values the more prononcoumced the roundedness
        gp = gpar(fill = fill, col = NA)
      ),
      xmin = 0, xmax = 6, ymin = 0, ymax = 3
    ) +
    annotate(
    "text", 
      x = 3, y = 1.5,
      hjust = 0.5, vjust = 0.5,
      size = 10, # text size for perc_change
      label = perc_change,
      color = 'white', 
      family = 'franklin',
      fontface = 'bold'
    ) +
    xlim(0, 6) + ylim(0, 3) +
    coord_fixed(ratio = 1) + 
    theme_void() +
    theme(
      plot.margin = margin(4, 2, 2, 2), # small margins add around each plot for more subtitle room
      plot.title = element_text(hjust = 0.5, size = 14, family = 'franklin-medium', margin = margin(b = 5)) # this is a midway font face between plain and bold
    )
}
# loop through labels, fills, and dimensions, and including 'label' for the title
tile_plots <- pmap(
  list(
    perc_change = p1_tribble$perc_change,
    label_text = p1_tribble$label, 
    fill = p1_tribble$fill,
    width_cm = p1_tribble$width_cm,
    length_cm = p1_tribble$length_cm
  ),
  tile_plot_rounded 
)

# assign one row so that all plots are side by side and not potentially stacked (vertically)
p1 <- wrap_plots(
  tile_plots, nrow = 1
) 
# adds after wrap a title
p_final <- p1 + 
  plot_annotation(
    title = 'Travel compared with last year',
    caption = 'Sources: U.S. Customs and Border Protection and the Airlines Reporting Corporation',
    theme = theme(
      plot.title = element_text(size = 20, family = 'franklin', face = "bold", hjust = 0.5, margin = margin(t = -10, b = 10)),
      plot.caption = element_text(size = 9, family = 'franklin-medium', face = 'bold', colour = '#727272', hjust = 0.15)
      )
  ) 
p_final

International arrivals at major U.S. airports

Placeholder for plot 2- line plots for Q1 2024 vs Q1 2025 in Intl. arrivals at major US airports

#|echo: false
#|message: false
#|warning: false
#|include: true

## p3; table to embed in gt() 
# read in the correspoinding table node
url <- 'https://www.nytimes.com/interactive/2025/04/30/world/us-travel-decline.html'
raw_table <- 
  read_html(url) |> 
  html_element(css = '.svelte-5z6yzk') |> 
  html_table() |> 
  select(country = 1, perc_change = 2) |> 
  filter(perc_change != '')

# processing the tibble
individual_country_changes <- 
  raw_table |> 
  mutate(
    perc_change_num = parse_number(perc_change),
    left_right_ended = if_else(perc_change_num <= -.02, 'left', 'right'),
    # below also going to be used to section off the table into 3 columns
    change_direction = case_when(
      between(perc_change_num, -2, 1) ~ 'stalled',
      perc_change_num < 0 ~ 'decreased',
      .default = 'increased') 
  ) |> 
  mutate(
    groupings = case_when(
      n() == 11 ~ 1,
      n() ==  6 ~ 2,
      .default = 3),
    .by = change_direction
    ) 

Confidence, but Warning Signs

For the below visualization, I opted to use gt() from gt package as it naturally comes to mind when trying to build ‘great tables’, yes pun intended. A few things to mention here to explain my thought process.

• Clearly the tables go over the left/right margins of the article layout in the original. While our dimensions may allow for all 3 tables to fit side by side, for replication purposes, we ‘push out’ the margins or reduce them to make them all 3 tables fit side by side.
  
• Second, one other thing that stands out is the clear customization of the third column, the percent change actual values, where a target bar plot of some sort is drawn. To replicate that, one way (potentially only way) is to use text_transform(), in tandem with a custom function using fn argument that will incorporate mostly css code. This allows for greater customization down to the minutiae.
  
• Said last column is also centered and anchored using a small vertical tick (this is most visible in the middle plot where values alternate between negative (left) and positive (right) of tick.
  
• From the tick each value is gauged on their max for that table, to get a sense of proportionality and make sure each bar’s no. of pixels proportionally match to the greatest (absolute) value per table.

#|echo: false
#|message: false
#|warning: false
#|include: false
#|results: 'asis' 

gt_table_custom_ <- function(hex, direction, title = NULL) {
  
  title_ <- title
  hex_to_pass_in_fn <- hex
  dir_to_pass_in_fn <- direction
  
  individual_country_changes |> 
    filter(groupings == {{direction}}) |> 
    select(1:3) |> 
    gt() |> 
    text_transform(
      locations = cells_body(columns = perc_change_num),
      fn = function(x) {
        x_num <- as.numeric(x)
        max_val <- max(abs(x_num), na.rm = TRUE)
        if(max_val == 0) max_val <- 1 
        widths_for_glue <- abs(x_num) / max_val * if (dir_to_pass_in_fn == 2) 8 else 100 # default scaling may look misleaading for 'no change' countries, so massively reduced here otherwise center table values get way inflated for what they are ([-2; 1])
        
        # loop thru values of perc_change column and isolate each element so that it can pass thru str_glue() without length(x) > 1 error from if/else statement
        purrr::map_chr(seq_along(x_num), function(x) {
          width <- widths_for_glue[x]
          val <- x_num[x]
          
          if(dir_to_pass_in_fn == 2) {
            direction_to_go <- if(val < 0) "right" else "left"
            translate_offset <- if(val <= -2) "20%" else "0" # translate back to the origin for values >= -.02 (this gets applied only for no-change countries/middle table)
            str_glue(
              '<div style="display: flex; align-items: center; justify-content: center;">
                 <div style="position: relative; width: 100%; height: 8px;">
                   <div style="position: absolute; left: 50%; top: -4px; bottom: 4px;width: 2px; height: 16px; background: #ccc;"></div>
                   <div style="position: absolute; {direction_to_go}: 50%; top: 0; 
                               background: {hex_to_pass_in_fn}; height: 8px; width: {width}%; 
                               transform: translateX({translate_offset}); 
                               border-radius: 3px;"></div>
                 </div>
               </div>'
            )
          }  # for below, we treat direction 1 (very negative change) and 2 (very positive) virtually the same, only former goes from center to left while latter from center to right
          else {
            direction_to_go <- if(direction == 1) "right" else "left"
            str_glue(
              '<div style="display: flex; align-items: center; justify-content: center;">
                 <div style="position: relative; width: 50%; height: 8px;">
                   <div style="position: absolute; left: 50%; top: -8px; width: 2px; height: 16px; background: #ccc; transform: translateX(-1px);"></div>
                   <div style="position: absolute; {direction_to_go}: 50%; top: -4px; background: {hex_to_pass_in_fn}; height: 8px; width: {width}%; border-radius: 3px;"></div>  
                 </div>
               </div>'
            )
          }
        })
      }
    ) |>
    cols_align(
      align = "center",
      columns = perc_change
    ) |> 
    tab_style(
      style = cell_text(color = hex),
      locations = cells_body(columns = perc_change)
    ) |> 
    tab_style(
      style = css("white-space" = "nowrap"),
      locations = cells_body(columns = country)
      ) |> 
    cols_label(
      country = md("**Country**"),
      perc_change = "",
perc_change_num = htmltools::HTML("<span style='white-space: nowrap;'><strong>Change vs. 2024</strong></span>")    
) |> 
    opt_table_font(
      font = c("franklin-medium"),
      weight = 500
    ) |> 
    tab_options(
      heading.align = "left",
      table.width = pct(100), 
      table.border.top.style = "hidden",             
      column_labels.border.bottom.style = "solid",
      column_labels.border.bottom.width = px(1),
      column_labels.border.bottom.color = "white",
    ) |> 
    cols_width(
    country ~ pct(50),
    perc_change ~ pct(50),
    perc_change_num ~ pct(50)
    ) 
}

# generate and assign tables below 
tbl1 <- gt_table_custom_(hex = '#d65f00', direction = 1) |> 
  tab_header(
    title = md("**Where U.S.-bound summer flight bookings have <span style='color: #d65f00;'> decreased </span>...**")
    ) |> 
    tab_options(
    heading.padding = px(8.7)
  )

tbl2 <- gt_table_custom_(hex = '#666666', direction = 2) |> 
  tab_header(
    title = md("<span style='color: #666666;'>**... stayed about the same ...**</span>"),
    subtitle = md("<span style='color: #FFFFFF;'>adding artificial padding.**</span>")
  ) |> 
    tab_options(
    heading.padding = px(6)
  )

tbl3 <- gt_table_custom_(hex = '#2b9d6c', direction = 3) |> 
    tab_header(
    title = md("**... and <span style='color: #2b9d6c;'>increased</span>.**"),
    subtitle = md("<span style='color: #FFFFFF;'>adding artificial.** padding</span>")
  ) |> 
    tab_options(
    heading.padding = px(6)
  )

tables_row_layout <- htmltools::tags$div(
  style = "display: flex; justify-content: space-between; align-items: flex-start; gap: 20px; width: 100%; padding: 0 10px;",
  htmltools::tags$div(style = "flex: 1; min-width: 0;", tbl1), 
  htmltools::tags$div(style = "flex: 1; min-width: 0;", tbl2),
  htmltools::tags$div(style = "flex: 1; min-width: 0;", tbl3)
)

caption_text <- "<span style='color:#707070;'>Source: Airlines Reporting Corporation &bull; Data covers flight bookings made between Feb. 1 and April 13 for travel between Memorial Day and Labor Day, compared to the same period last year.</span>"
caption_html_content <- HTML(caption_text) 
left_padding_value <- "20px" # adding some padding so that caption moves slightl to the right to match original's caption poisiton 

caption_styled_div <- htmltools::tags$div(
  style = paste(
    "width: 50%;",  # overall visual span
    "max-width: 1000px;", # optional cap the max width of the caption
    "margin: 5px auto 0 auto;", 
    "text-align: left;",
    "font-family: 'franklin-medium', 'Libre Franklin', Arial, sans-serif;", # prioritize 'franklin-medium'
    "font-weight: 500;",                                                 
    "font-size: 0.85em;", 
    "line-height: 1.1;",
    paste0("padding-left: ", left_padding_value, ";") 
  ),
  caption_html_content
)

# parent div will stack them vertically and can be used to constrain overall width.
overall_final_layout <- htmltools::tags$div(
  style = "width: 100%; max-width: 1900px; margin: 0 auto; padding-bottom: 1px;",
  tables_row_layout,
  caption_styled_div
)
# final output
overall_final_layout

Where U.S.-bound summer flight bookings have decreased …
Country		Change vs. 2024
Canada	-21%
Netherlands	-17%
Germany	-12%
Ecuador	-11%
Mexico	-9%
Dom. Rep.	-9%
Switzerland	-8%
China	-7%
South Korea	-5%
India	-4%
Poland	-4%

… stayed about the same …
adding artificial padding.**
Country		Change vs. 2024
U.K.	-2%
France	-2%
Colombia	-2%
Italy	0%
Philippines	+1%
Greece	+1%

… and increased.
adding artificial.** padding
Country		Change vs. 2024
Costa Rica	+3%
Brazil	+4%
Australia	+6%
Spain	+8%
Portugal	+8%
Japan	+11%
Ireland	+11%
Argentina	+39%

Source: Airlines Reporting Corporation • Data covers flight bookings made between Feb. 1 and April 13 for travel between Memorial Day and Labor Day, compared to the same period last year.

Unlike the line graphs from earlier, showing the Intl. Arrivals, where the graph was embedded as an image, here I was able to extract the actual data using the tag id pointing out to the table using Selector Gadget - a free reliable Chrome Extension that can be used to identify either the CSS selector or Xpath of site elements - in tandem with rvest to read in the embedded data from said selector into a tibble.
Note that within gtExtras, there already is an New York Times custom theme table using gtExtras::gt_theme_nytimes(), but did not closely match the style of the headers or the overall style of the tables; but it’s good to know if you are working with your own d¬ata (not replicating another piece), and you’re just ready to use a starter template or its boilerplate to at least test how it looks, if nothing else.
Overall, I think this is a fairly solid replication of the tables from the original, a piece I’ve enjoyed creating as it involved fairly advanced customization - overall it was deceptively simple to re-create, but worth it.
One thing to call out however is that these tables are optimized for desktop viewing due to their 100% width formatting. Mobile users may experience cramped display issues.

A Canadian boycott

#|echo: false
#|message: false
#|warning: false
#|include: false

theme_set_custom()

car_xing_ca_us_borders_py <- read_csv('/Users/kardouskarrim/Desktop/car_crossing_ca_us_borders.csv') |>
  distinct()

car_xing_ca_us_borders_cy <- read_csv('/Users/kardouskarrim/Desktop/car_crossing_ca_us_borders2.csv') |> 
  distinct()

# i am testing with below filters 
# 1st, 2nd filter should only focus on Canadian nationals and or residents (article focuses on Canadian sentiment; with use of strong word such as boycott, so assuming we should look into Canada returns only, not Intl. returns from US into Canada)
# 3nd filter -> 'Vehicles' is a major group (when 'automobiles' and 'motorcycles' are subgroups) so filtering to 'Vehicles' so that not to inflate the counts, but the plot caption in the original article mentions 'by automobile' so we filter t
# 4rd filter same idea as in 2nd but here -> 'Travellers' is a major group after a quick calculation for one day as an example where all values for that day sumn up to 'Travellers' value 

filtered_to_canadian_cy <-
  car_xing_ca_us_borders_cy |> 
  filter(
    `Traveller characteristics` == 'Canadian resident visitors returning to Canada' & `Vehicle licence plate` == 'Canadian-plated vehicles entering Canada' & GEO == 'Canada' &
    `Vehicle type` == 'Automobiles' & `Traveller type` == 'Travellers'
    ) |> 
  # above combination of filters is giving me exactly 1 record per day (without any aggregations/roll ups) and it also gives me 116 days (Jan 1 through to Apr 26th, so we stick to those filters; likely correct)
  select(
    crossing_date = 1,
    travel_characteristic = 6,
    travel_type = 7, 
    vehicle_ttl = ncol(car_xing_ca_us_borders_cy) - 4
    ) 

# same with py (or prior year)
filtered_to_canadian_py <- 
  car_xing_ca_us_borders_py |> 
    filter(
    `Traveller characteristics` == 'Canadian resident visitors returning to Canada' & `Vehicle licence plate` == 'Canadian-plated vehicles entering Canada' & GEO == 'Canada' &
    `Vehicle type` == 'Automobiles' & `Traveller type` == 'Travellers'
    ) |> 
  select(
    crossing_date = 1,
    travel_characteristic = 6,
    travel_type = 7, 
    vehicle_ttl = ncol(car_xing_ca_us_borders_py) - 4
    ) |> # 117 days for py since it was leap year, so we filter out feb 29th to have a day over day comparison
  filter(crossing_date != '2024-02-29')
  
# combining both sets 
car_xing_change <- 
  filtered_to_canadian_cy |> 
  select(1, last_col()) |> 
  arrange(crossing_date) |> 
  bind_cols(
    filtered_to_canadian_py |> 
      arrange(crossing_date) |> 
      select(ly_ttls = last_col())
  ) |> 
  mutate(
    change = vehicle_ttl - ly_ttls,
    change_perc = (vehicle_ttl - ly_ttls) / ly_ttls
    ) |> 
    filter(crossing_date < '2025-04-10') |> 
  # add a rolling 7 day avg
  mutate(
    rolling_avg_7day = zoo::rollmean(change_perc, k = 7, fill = NA, na.rm = TRUE)
    ) |> 
  filter(!is.na(rolling_avg_7day))

# first two months get abbreviated followed by a dot, while last two don't get abbreviated
month_custom_labeller <- 
  map_chr(
  .x = car_xing_change |> 
    pull(crossing_date) |> 
    lubridate::month() |> 
    unique() |> 
    sort(), 
  .f = function(x) 
    case_when(
      x > 2 ~ month(x, label = TRUE, abbr = FALSE),
      .default = paste0(month(x, label = TRUE, abbr = TRUE), '.')
      )
  )
  
caption_text <- "<span style='color:#707070;'>Sources: Statistics Canada/K. Kardous Note: Data shows journeys by Canadian residents returning to Canada from the<br> U.S. by automobile. A 7-day rolling average is shown.</span>"

car_xing_change |> 
    mutate(
        y_pos = pmax(rolling_avg_7day, 0),  # positive values only; this caps at 0 for < 0 so to create essentially two seperate series (one pos. and one negative)
        y_neg = pmin(rolling_avg_7day, 0)   # negative values only
    ) |> 
  ggplot(aes(x = crossing_date)) + 
  geom_area(aes(y = y_pos), fill = '#2b9d6c', outline.type = 'lower') +
  geom_area(aes(y = y_neg), fill = '#d65f00', outline.type = 'lower') +
  geom_hline(yintercept = 0) + 
  labs(
    x = NULL, 
    y = NULL,
    caption = caption_text
    ) + 
  scale_y_continuous(
    labels = scales::percent
    ) + 
  scale_x_date(
    breaks = seq(as.Date('2025-01-01'), as.Date('2025-04-09'), 'm'),
    labels = month_custom_labeller
    ) +
  # arrow and text for negative region
  annotate(
    geom = "segment",
    x = as.Date('2025-04-04'),    
    xend = as.Date('2025-04-04'), 
    y = -0.02,                    
    yend = -0.1,                 
    colour = "black",             
    arrow = arrow(length = unit(0.1, "cm"), angle = 50, type = 'open'),
    linewidth = .35
  ) + 
  annotate(
    geom = 'text',
    x = as.Date('2025-04-02'),
    y = -0.06,
    label = 'Less travel\nthan last year', 
    hjust = 1,
  ) +
  # arrow and text for positive region
    annotate(
    geom = "segment",
    x = as.Date('2025-04-04'),    
    xend = as.Date('2025-04-04'), 
    y = 0.025,                    
    yend = 0.1,                 
    colour = "black",             
    arrow = arrow(length = unit(0.1, "cm"), angle = 50, type = 'open'),
    linewidth = .35
  ) +
    annotate(
    geom = 'text',
    x = as.Date('2025-04-02'),
    y = 0.06,
    label = 'More travel\nthan last year', 
    hjust = 1
    ) + 
  theme(
    panel.grid.minor.x = element_blank(),
    panel.grid.major.x = element_blank(),
    plot.caption = element_markdown(
      size = 8, hjust = 0, 
      margin = margin(l = -20, r = 10, t = 10, b = -5)
      ),
    axis.text = element_text(family = 'franklin-medium', face = 'bold'),
    axis.text.x = element_text(hjust = -.9)
  ) 

Big drops along the border